预测青少年自杀企图趋势:使用谷歌趋势和历史数据的机器学习分析
Zofia Kachlik1, Michał Walaszek1, Wojciech Nazar1
1Department of Psychiatry, Faculty of Medicine, Medical University of Gdansk, 80-214 Gdańsk, Poland.
Journal of clinical medicine
|September 27, 2025
概括
使用谷歌趋势数据的机器学习模型可以帮助预测青少年自杀企图. 这种方法对儿童群体的实时风险识别非常有希望.
科学领域:
- 计算精神病学是一种计算精神病学.
- 数字流行病学数字流行病学
- 机器学习在公共卫生中的应用
背景情况:
- 自杀是年轻人死亡的主要原因,可用的预测工具有限.
- 在18岁以下的个体中预测自杀企图 (SA) 仍然是一个重大挑战.
- 本研究探讨了使用谷歌趋势数据在儿科患者中进行SA预测的方法.
研究的目的:
- 开发和评估机器学习 (ML) 模型来预测青少年的SA.
- 通过在线搜索数据识别SA的可靠预测因素.
- 评估使用谷歌趋势用于实时自杀风险监测的可行性.
主要方法:
- 从谷歌趋势中对与自杀风险相关的术语的相对搜索量 (RSV) 的分析.
- 使用皮尔森相关系数 (PCC) 识别与SA利率有很强的相关性.
- 开发和评估ML模型,包括随机森林回归,支持矢量回归 (SVR),XGBoost和线性回归,由PCC,MAE,MSE,RMSE和MAPE评估.
主要成果:
- 诸如"精神病医生"和"焦虑障碍"等术语与SA率 (PCC ≥0.90) 有着强烈的相关性.
- 随机森林回归表现最好 (PCC = 0.953),确定"倦怠"",焦虑症"",抗抑郁药"和"精神科医生"作为关键预测因素.
- 其他模型显示出不同的性能:XGBoost (PCC = 0.446),SVR (PCC = 0.833) 和线性回归 (PCC = 0.947).
结论:
- 使用谷歌趋势数据的ML模型显示了短期预测青年SA的潜力.
- 在线搜索数据可以成为识别儿童群体实时自杀风险的有价值工具.
- 需要进一步的研究来完善这些预测模型并将其整合到公共卫生战略中.
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